Background

Data from epidemiological and animal model studies suggest that nutrition during pregnancy may affect the health status of subsequent generations. These transgenerational effects are now being explained by disruptions at the level of the epigenetic machinery. Besides in vitro environmental exposures, the possible impact on the reprogramming of methylation profiles at imprinted genes at a much earlier time point, such as during spermatogenesis or oogenesis, has not previously been considered. In this study, our aim was to determine associations between preconceptional obesity and DNA methylation profiles in the offspring, particularly at the differentially methylated regions (DMRs) of the imprinted Insulin-like Growth Factor 2 (IGF2) gene.

Methods

We examined DNA from umbilical cord blood leukocytes from 79 newborns, born between July 2005 and November 2006 at Duke University Hospital, Durham, NC. Their mothers participated in the Newborn Epigenetics Study (NEST) during pregnancy. Parental characteristics were obtained via standardized questionnaires and medical records. DNA methylation patterns at two DMRs were analyzed by bisulfite pyrosequencing; one DMR upstream of IGF2 (IGF2 DMR), and one DMR upstream of the neighboring H19 gene (H19 DMR). Multiple regression models were used to determine potential associations between the offspring's DNA methylation patterns and parental obesity before conception. Obesity was defined as body mass index (BMI) ≥30 kg/m2.

Results

Hypomethylation at the IGF2 DMR was associated with paternal obesity. Even after adjusting for several maternal and newborn characteristics, we observed a persistent inverse association between DNA methylation in the offspring and paternal obesity (β-coefficient was -5.28, P = 0.003). At the H19 DMR, no significant associations were detected between methylation patterns and paternal obesity. Our data suggest an increase in DNA methylation at the IGF2 and H19 DMRs among newborns from obese mothers, but a larger study is warranted to further explore the potential effects of maternal obesity or lifestyle on the offspring's epigenome.

Conclusions

While our small sample size is limited, our data indicate a preconceptional impact of paternal obesity on the reprogramming of imprint marks during spermatogenesis. Given the biological importance of imprinting fidelity, our study provides evidence for transgenerational effects of paternal obesity that may influence the offspring's future health status.

The relationship between the parental genomes in terms of the future growth and development of their offspring is not critical. For the majority of the genome the tissue-specific gene expression and epigenetic status is shared between the parents equally, with both alleles contributing without parental bias. For a very small number of genes the rules change and control of expression is restricted to a specific, parentally derived allele, a phenomenon known as genomic imprinting. The insulin-like growth factor 2 (Igf2/IGF2) is a robustly imprinted gene, important for fetal growth in both mice and humans. In utero IGF2 exhibits paternal expression, which is controlled by several mechanisms, including the maternally expressing untranslated H19 gene. In the study by Soubry et al., a correlation is drawn between the IGF2 methylation status in fetal cord blood leucocytes, and the obesity status of the father from whom the active IGF2 allele is derived through his sperm. These data imply that paternal obesity affects the normal IGF2 methylation in the sperm and this in turn alters the expression of IGF2 in the baby.

A recent preclinical study has shown that not only maternal smoking but also grandmaternal smoking is associated with elevated pediatric asthma risk. Using a well-established rat model of in utero nicotine exposure, Rehan et al. have now demonstrated multigenerational effects of nicotine that could explain this 'grandmother effect'. F1 offspring of nicotine-treated pregnant rats exhibited asthma-like changes to lung function and associated epigenetic changes to DNA and histones in both lungs and gonads. These alterations were blocked by co-administration of the peroxisome proliferator-activated receptor-γ agonist, rosiglitazone, implicating downregulation of this receptor in the nicotine effects. F2 offspring of F1 mated animals exhibited similar changes in lung function to that of their parents, even though they had never been exposed to nicotine. Thus epigenetic mechanisms appear to underlie the multigenerational transmission of a nicotine-induced asthma-like phenotype. These findings emphasize the need for more effective smoking cessation strategies during pregnancy, and cast further doubt on the safety of using nicotine replacement therapy to reduce tobacco use in pregnant women.

Please see related article: http://www.biomedcentral.com/1741-7015/10/129

Background

There is growing interest in realist synthesis as an alternative systematic review method. This approach offers the potential to expand the knowledge base in policy-relevant areas - for example, by explaining the success, failure or mixed fortunes of complex interventions. No previous publication standards exist for reporting realist syntheses. This standard was developed as part of the RAMESES (Realist And MEta-narrative Evidence Syntheses: Evolving Standards) project. The project's aim is to produce preliminary publication standards for realist systematic reviews.

Methods

We (a) collated and summarized existing literature on the principles of good practice in realist syntheses; (b) considered the extent to which these principles had been followed by published syntheses, thereby identifying how rigor may be lost and how existing methods could be improved; (c) used a three-round online Delphi method with an interdisciplinary panel of national and international experts in evidence synthesis, realist research, policy and/or publishing to produce and iteratively refine a draft set of methodological steps and publication standards; (d) provided real-time support to ongoing realist syntheses and the open-access RAMESES online discussion list so as to capture problems and questions as they arose; and (e) synthesized expert input, evidence syntheses and real-time problem analysis into a definitive set of standards.

Results

We identified 35 published realist syntheses, provided real-time support to 9 on-going syntheses and captured questions raised in the RAMESES discussion list. Through analysis and discussion within the project team, we summarized the published literature and common questions and challenges into briefing materials for the Delphi panel, comprising 37 members. Within three rounds this panel had reached consensus on 19 key publication standards, with an overall response rate of 91%.

Conclusion

This project used multiple sources to develop and draw together evidence and expertise in realist synthesis. For each item we have included an explanation for why it is important and guidance on how it might be reported. Realist synthesis is a relatively new method for evidence synthesis and as experience and methodological developments occur, we anticipate that these standards will evolve to reflect further methodological developments. We hope that these standards will act as a resource that will contribute to improving the reporting of realist syntheses.

To encourage dissemination of the RAMESES publication standards, this article is co-published in the Journal of Advanced Nursing and is freely accessible on Wiley Online Library (http://www.wileyonlinelibrary.com/journal/jan).

Please see related article http://www.biomedcentral.com/1741-7015/11/20 and http://www.biomedcentral.com/1741-7015/11/22

Background

Meta-narrative review is one of an emerging menu of new approaches to qualitative and mixed-method systematic review. A meta-narrative review seeks to illuminate a heterogeneous topic area by highlighting the contrasting and complementary ways in which researchers have studied the same or a similar topic. No previous publication standards exist for the reporting of meta-narrative reviews. This publication standard was developed as part of the RAMESES (Realist And MEta-narrative Evidence Syntheses: Evolving Standards) project. The project's aim is to produce preliminary publication standards for meta-narrative reviews.

Methods

We (a) collated and summarized existing literature on the principles of good practice in meta-narrative reviews; (b) considered the extent to which these principles had been followed by published reviews, thereby identifying how rigor may be lost and how existing methods could be improved; (c) used a three-round online Delphi method with an interdisciplinary panel of national and international experts in evidence synthesis, meta-narrative reviews, policy and/or publishing to produce and iteratively refine a draft set of methodological steps and publication standards; (d) provided real-time support to ongoing meta-narrative reviews and the open-access RAMESES online discussion list so as to capture problems and questions as they arose; and (e) synthesized expert input, evidence review and real-time problem analysis into a definitive set of standards.

Results

We identified nine published meta-narrative reviews, provided real-time support to four ongoing reviews and captured questions raised in the RAMESES discussion list. Through analysis and discussion within the project team, we summarized the published literature, and common questions and challenges into briefing materials for the Delphi panel, comprising 33 members. Within three rounds this panel had reached consensus on 20 key publication standards, with an overall response rate of 90%.

Conclusion

This project used multiple sources to draw together evidence and expertise in meta-narrative reviews. For each item we have included an explanation for why it is important and guidance on how it might be reported. Meta-narrative review is a relatively new method for evidence synthesis and as experience and methodological developments occur, we anticipate that these standards will evolve to reflect further theoretical and methodological developments. We hope that these standards will act as a resource that will contribute to improving the reporting of meta-narrative reviews.

To encourage dissemination of the RAMESES publication standards, this article is co-published in the Journal of Advanced Nursing and is freely accessible on Wiley Online Library (http://www.wileyonlinelibrary.com/journal/jan).

Please see related articles http://www.biomedcentral.com/1741-7015/11/21 and http://www.biomedcentral.com/1741-7015/11/22

Cigarette smoking is one of the most important causes of morbidity and mortality in the general population, and is a well-recognized risk factor for a variety of serious clinical conditions, including cardiovascular diseases, pulmonary diseases and cancers.

Smoking-related morbidity and mortality are of particular concern in patients with HIV infection, as the prevalence of current cigarette smoking is higher among HIV-positive patients than among the general population.

In a study by De et al., it has been evidenced that smoking is a risk factor for bacterial pneumonia in HIV-positive patients and smoking cessation reduces this risk.

HIV-positive patients who smoke have significantly increased mortality compared to those who have never smoked, indicating that smoking confers different mortality risk in HIV-positive as compared to HIV-negative patients, and lifestyle-related factors may pose a greater hazard to long-term survival of HIV-positive patients than those related to the HIV infection per se.

The high prevalence of smoking among HIV population, the many health risks that can result from this behavior, and the proven efficacy of cessation interventions in HIV-positive patients should encourage HIV care providers to make smoking cessation a high priority.

Background

Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.

Methods

On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.

Results

Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with 'low cancer-risk' characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring 'high cancer-risk" characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest 'high cancer- risk' cluster were different than those contributing to the classifiers for the 'low cancer-risk' clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.

Conclusions

The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs.

Background

Diagnosing serious infections in children is challenging, because of the low incidence of such infections and their non-specific presentation early in the course of illness. Prediction rules are promoted as a means to improve recognition of serious infections. A recent systematic review identified seven clinical prediction rules, of which only one had been prospectively validated, calling into question their appropriateness for clinical practice. We aimed to examine the diagnostic accuracy of these rules in multiple ambulatory care populations in Europe.

Methods

Four clinical prediction rules and two national guidelines, based on signs and symptoms, were validated retrospectively in seven individual patient datasets from primary care and emergency departments, comprising 11,023 children from the UK, the Netherlands, and Belgium. The accuracy of each rule was tested, with pre-test and post-test probabilities displayed using dumbbell plots, with serious infection settings stratified as low prevalence (LP; <5%), intermediate prevalence (IP; 5 to 20%), and high prevalence (HP; >20%) . In LP and IP settings, sensitivity should be >90% for effective ruling out infection.

Results

In LP settings, a five-stage decision tree and a pneumonia rule had sensitivities of >90% (at a negative likelihood ratio (NLR) of < 0.2) for ruling out serious infections, whereas the sensitivities of a meningitis rule and the Yale Observation Scale (YOS) varied widely, between 33 and 100%. In IP settings, the five-stage decision tree, the pneumonia rule, and YOS had sensitivities between 22 and 88%, with NLR ranging from 0.3 to 0.8. In an HP setting, the five-stage decision tree provided a sensitivity of 23%. In LP or IP settings, the sensitivities of the National Institute for Clinical Excellence guideline for feverish illness and the Dutch College of General Practitioners alarm symptoms ranged from 81 to 100%.

Conclusions

None of the clinical prediction rules examined in this study provided perfect diagnostic accuracy. In LP or IP settings, prediction rules and evidence-based guidelines had high sensitivity, providing promising rule-out value for serious infections in these datasets, although all had a percentage of residual uncertainty. Additional clinical assessment or testing such as point-of-care laboratory tests may be needed to increase clinical certainty. None of the prediction rules identified seemed to be valuable for HP settings such as emergency departments.

Background

Risk-stratified treatment recommendations facilitate treatment decision-making that balances patient-specific risks and preferences. It is unclear if and how such recommendations are developed in clinical practice guidelines (CPGs). Our aim was to assess if and how CPGs develop risk-stratified treatment recommendations for the prevention or treatment of common chronic diseases.

Methods

We searched the United States National Guideline Clearinghouse for US, Canadian and National Institute for Health and Clinical Excellence (United Kingdom) CPGs for heart disease, stroke, cancer, chronic obstructive pulmonary disease and diabetes that make risk-stratified treatment recommendations. We included only those CPGs that made risk-stratified treatment recommendations based on risk assessment tools. Two reviewers independently identified CPGs and extracted information on recommended risk assessment tools; type of evidence about treatment benefits and harms; methods for linking risk estimates to treatment evidence and for developing treatment thresholds; and consideration of patient preferences.

Results

We identified 20 CPGs that made risk-stratified treatment recommendations out of 133 CPGs that made any type of treatment recommendations for the chronic diseases considered in this study. Of the included 20 CPGs, 16 (80%) used evidence about treatment benefits from randomized controlled trials, meta-analyses or other guidelines, and the source of evidence was unclear in the remaining four (20%) CPGs. Nine CPGs (45%) used evidence on harms from randomized controlled trials or observational studies, while 11 CPGs (55%) did not clearly refer to harms. Nine CPGs (45%) explained how risk prediction and evidence about treatments effects were linked (for example, applying estimates of relative risk reductions to absolute risks), but only one CPG (5%) assessed benefit and harm quantitatively and three CPGs (15%) explicitly reported consideration of patient preferences.

Conclusions

Only a small proportion of CPGs for chronic diseases make risk-stratified treatment recommendations with a focus on heart disease and stroke prevention, diabetes and breast cancer. For most CPGs it is unclear how risk-stratified treatment recommendations were developed. As a consequence, it is uncertain if CPGs support patients and physicians in finding an acceptable benefit- harm balance that reflects both profile-specific outcome risks and preferences.

Background

Since 2009, several studies have identified single-nucleotide polymorphisms (SNPs) near the gene encoding for interleukin (IL)-28 (IL28B) that are strongly associated with spontaneous and treatment-induced hepatitis C virus (HCV) clearance. Because this large amount of data includes some inconsistencies, we consider assessment of the global estimate for each SNP to be essential.

Methods

Relevant studies assessing IL28B polymorphisms associated with sustained virologic response (SVR) and spontaneous clearance (SC) were identified from a literature search of PubMed up to 9 July, 2012. Studies were eligible studies if they included patients infected with HCV or HCV/HIV, or assessed any SNP located within or near the IL28B gene, SVR data available under standard treatment, and/or SC data in patients with acute HCV infection. Pooled odds ratios were estimated by fixed or random effects models when appropriate. Variables such as HCV genotype, ethnicity, and type of co-infection were studied.

Results

Of 282 screened studies, 67 were selected for SVR and 10 for SC. In total, 20,163 patients were studied for SVR and 3,554 for SC. For SVR, we found that all SNPs showed strong associations in patients with HCV genotypes 1 and 4, whereas the pooled ORs were almost three times lower for genotypes 2 and 3 (rs12979860 and rs8099917). Regarding ethnicity, the SNP most associated with SVR was rs12979860 in white patients, whereas in East Asians it seemed to be rs8099917. The most studied SNP (rs12979860) showed similar results for patients co-infected with HCV/HIV, as for those infected with HCV only. Finally, rs12979860 and rs8099917 both appeared to be associated with SC.

Conclusions

IL28B polymorphisms influence both the outcome of interferon treatment and the natural clearance of HCV. However we did not identify a universal predictor SNP, as the best genetic markers differed depending on patient ethnicity, genotype, and type of infection. Nevertheless, our results may be useful for more precise treatment decision-making.

Background

Mounting evidence has suggested that plasminogen activator inhibitor-1 (PAI-1) is a candidate for increased risk of diabetic retinopathy. Studies have reported that insertion/deletion polymorphism in the PAI-1 gene may influence the risk of this disease. To comprehensively address this issue, we performed a meta-analysis to evaluate the association of PAI-1 4G/5G polymorphism with diabetic retinopathy in type 2 diabetes.

Methods

Data were retrieved in a systematic manner and analyzed using Review Manager and STATA Statistical Software. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of associations.

Results

Nine studies with 1, 217 cases and 1, 459 controls were included. Allelic and genotypic comparisons between cases and controls were evaluated. Overall analysis suggests a marginal association of the 4G/5G polymorphism with diabetic retinopathy (for 4G versus 5G: OR 1.13, 95%CI 1.01 to 1.26; for 4G/4G versus 5G/5G: OR 1.30, 95%CI 1.04 to 1.64; for 4G/4G versus 5G/5G + 4G/5G: OR 1.26, 95%CI 1.05 to 1.52). In subgroup analysis by ethnicity, we found an association among the Caucasian population (for 4G versus 5G: OR 1.14, 95% CI 1.00 to 1.30; for 4G/4G versus 5G/5G: OR 1.33, 95%CI 1.02 to 1.74; for 4G/4G versus 5G/5G + 4G/5G: OR 1.41, 95%CI 1.13 to 1.77). When stratified by the average duration of diabetes, patients with diabetes histories longer than 10 years have an elevated susceptibility to diabetic retinopathy than those with shorter histories (for 4G/4G versus 5G/5G: OR 1.47, 95%CI 1.08 to 2.00). We also detected a higher risk in hospital-based studies (for 4G/4G versus 5G/5G+4G/5G: OR 1.27, 95%CI 1.02 to 1.57).

Conclusions

The present meta-analysis suggested that 4G/5G polymorphism in the PAI-1 gene potentially increased the risk of diabetic retinopathy in type 2 diabetes and showed a discrepancy in different ethnicities. A higher susceptibility in patients with longer duration of diabetes (more than 10 years) indicated a gene-environment interaction in determining the risk of diabetic retinopathy.

Background

Despite considerable progress in the development of anticancer therapies, there is still a high mortality rate caused by cancer relapse and metastasis. Dormant or slow-cycling residual tumor cells are thought to be a source of tumor relapse and metastasis, and are therefore an obstacle to therapy. In this study, we assessed the drug resistance of tumor cells in mice, and investigated whether vaccination could promote survival.

Methods

The mouse colon carcinoma cell line CT-26 was treated with 5-fluorouracil to assess its sensitivity to drug treatment. Mice with colon tumors were immunized with inactivated slow-cycling CT-26 cells to estimate the efficacy of this vaccine.

Results

We identified a small population of slow-cycling tumor cells in the mouse colon carcinoma CT-26 cell line, which was resistant to conventional chemotherapy. To inhibit tumor recurrence and metastasis more effectively, treatments that selectively target the slow-cycling tumor cells should be developed to complement conventional therapies. We found that drug-treated, slow-cycling tumor cells induced a more intense immune response in vitro. Moreover, vaccination with inactivated slow-cycling tumor cells caused a reduction in tumor volume and prolonged the overall survival of tumor-bearing mice.

Conclusions

These findings suggest that targeting of slow-cycling tumor cells application using immunotherapy is a possible treatment to complement traditional antitumor therapy.

Background

During the extremely challenging 4,487 km ultramarathon TransEurope-FootRace 2009, runners showed considerable reduction of body weight. The effects of this endurance run on brain volume changes but also possible formation of brain edema or new lesions were explored by repeated magnetic resonance imaging (MRI) studies.

Methods

A total of 15 runners signed an informed consent to participate in this study of planned brain scans before, twice during, and about 8 months after the race. Because of dropouts, global gray matter volume analysis could only be performed in ten runners covering three timepoints, and in seven runners who also had a follow-up scan. Scanning was performed on three identical 1.5 T Siemens MAGNETOM Avanto scanners, two of them located at our university. The third MRI scanner with identical sequence parameters was a mobile MRI unit escorting the runners. Volumetric 3D datasets were acquired using a magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence. Additionally, diffusion-weighted (DWI) and fluid attenuated inversion recovery (FLAIR) imaging was performed.

Results

Average global gray matter volume as well as body weight significantly decreased by 6% during the race. After 8 months, gray matter volume returned to baseline as well as body weight. No new brain lesions were detected by DWI or FLAIR imaging.

Conclusions

Physiological brain volume reduction during aging is less than 0.2% per year. Therefore a volume reduction of about 6% during the 2 months of extreme running appears to be substantial. The reconstitution in global volume measures after 8 months shows the process to be reversible. As possible mechanisms we discuss loss of protein, hypercortisolism and hyponatremia to account for both substantiality and reversibility of gray matter volume reductions. Reversible brain volume reduction during an ultramarathon suggests that extreme running might serve as a model to investigate possible mechanisms of transient brain volume changes. However, despite massive metabolic load, we found no new lesions in trained athletes participating in a multistage ultramarathon.

See related commentary http://www.biomedcentral.com/1741-7015/10/171

Physical activity, likely through induction of neuroplasticity, is a promising intervention to promote brain health. In athletes it is clear that training can and does, by physiological adaptations, extend the frontiers of performance capacity. The limits of our endurance capacity lie deeply in the human brain, determined by various personal factors yet to be explored. The human brain, with its vast neural connections and its potential for seemingly endless behaviors, constitutes one of the final frontiers of medicine. In a recent study published in BMC Medicine, the TransEurope FootRace Project followed 10 ultra-endurance runners over around 4,500 km across Europe and recorded a large data collection of brain imaging scans. This study indicates that the cerebral atrophy amounting to a reduction of approximately 6% throughout the two months of the race is reversed upon follow-up. While this study will contribute to advances in the limits of human performance on the neurophysiological processes in sports scientists, it will also bring important understanding to clinicians about cerebral atrophy in people who are vulnerable to physical and psychological stress long term.

See related research article http://www.biomedcentral.com/1741-7015/10/170

Background

Cycling is considered to be a highly beneficial sport for significantly enhancing cardiovascular fitness in individuals, yet studies show little or no corresponding improvements in bone mass.

Methods

A scientific literature search on studies discussing bone mass and bone metabolism in cyclists was performed to collect all relevant published material up to April 2012. Descriptive, cross-sectional, longitudinal and interventional studies were all reviewed. Inclusion criteria were met by 31 studies.

Results

Heterogeneous studies in terms of gender, age, data source, group of comparison, cycling level or modality practiced among others factors showed minor but important differences in results. Despite some controversial results, it has been observed that adult road cyclists participating in regular training have low bone mineral density in key regions (for example, lumbar spine). Conversely, other types of cycling (such as mountain biking), or combination with other sports could reduce this unsafe effect. These results cannot yet be explained by differences in dietary patterns or endocrine factors.

Conclusions

From our comprehensive survey of the current available literature it can be concluded that road cycling does not appear to confer any significant osteogenic benefit. The cause of this may be related to spending long hours in a weight-supported position on the bike in combination with the necessary enforced recovery time that involves a large amount of time sitting or lying supine, especially at the competitive level.

Cycling has been shown to confer considerable benefits in terms of health, leading to reductions in death rates principally due to cardiovascular improvements and adaptation.

Given the disparity between the benefits of cycling on cardiovascular fitness and previous research finding that cycling may not be beneficial for bone health, Hugo Olmedillas and colleagues performed a systematic review of the literature. They concluded that road cycling does not appear to confer any significant osteogenic benefit. They postulate that the cause of this is that, particularly at a competitive level, riders spend long periods of time in a weight-supported position on the bike.

Training programs may be supplemented with impact loading to preserve bone health; however, the small increased risk of soft tissue injury must also be considered.

See related commentary http://www.biomedcentral.com/1741-7015/10/168

Background

Musculoskeletal conditions (MSCs) are widely prevalent in present-day society, with resultant high healthcare costs and substantial negative effects on patient health and quality of life. The main aim of this overview was to synthesize evidence from systematic reviews on the effects of exercise therapy (ET) on pain and physical function for patients with MSCs. In addition, the evidence for the effect of ET on disease pathogenesis, and whether particular components of exercise programs are associated with the size of the treatment effects, was also explored.

Methods

We included four common conditions: fibromyalgia (FM), low back pain (LBP), neck pain (NP), and shoulder pain (SP), and four specific musculoskeletal diseases: osteoarthritis (OA), rheumatoid arthritis (RA), ankylosing spondylitis (AS), and osteoporosis (OP). We first included Cochrane reviews with the most recent update being January 2007 or later, and then searched for non-Cochrane reviews published after this date. Pain and physical functioning were selected as primary outcomes.

Results

We identified 9 reviews, comprising a total of 224 trials and 24,059 patients. In addition, one review addressing the effect of exercise on pathogenesis was included. Overall, we found solid evidence supporting ET in the management of MSCs, but there were substantial differences in the level of research evidence between the included diagnostic groups. The standardized mean differences for knee OA, LBP, FM, and SP varied between 0.30 and 0.65 and were significantly in favor of exercise for both pain and function. For NP, hip OA, RA, and AS, the effect estimates were generally smaller and not always significant. There was little or no evidence that ET can influence disease pathogenesis. The only exception was for osteoporosis, where there was evidence that ET increases bone mineral density in postmenopausal women, but no significant effects were found for clinically relevant outcomes (fractures). For LBP and knee OA, there was evidence suggesting that the treatment effect increases with the number of exercise sessions.

Conclusions

There is empirical evidence that ET has beneficial clinical effects for most MSCs. Except for osteoporosis, there seems to be a gap in the understanding of the ways in which ET influences disease mechanisms.

Background

Endurance exercise capacity diminishes under hot environmental conditions. Time to exhaustion can be increased by lowering body temperature prior to exercise (pre-cooling). This systematic literature review synthesizes the current findings of the effects of pre-cooling on endurance exercise performance, providing guidance for clinical practice and further research.

Methods

The MEDLINE, EMBASE, CINAHL, Web of Science and SPORTDiscus databases were searched in May 2012 for studies evaluating the effectiveness of pre-cooling to enhance endurance exercise performance in hot environmental conditions (≥ 28°C). Studies involving participants with increased susceptibility to heat strain, cooling during or between bouts of exercise, and protocols where aerobic endurance was not the principle performance outcome were excluded. Potential publications were assessed by two independent reviewers for inclusion and quality. Means and standard deviations of exercise performance variables were extracted or sought from original authors to enable effect size calculations.

Results

In all, 13 studies were identified. The majority of studies contained low participant numbers and/or absence of sample size calculations. Six studies used cold water immersion, four crushed ice ingestion and three cooling garments. The remaining study utilized mixed methods. Large heterogeneity in methodological design and exercise protocols was identified. Effect size calculations indicated moderate evidence that cold water immersion effectively improved endurance performance, and limited evidence that ice slurry ingestion improved performance. Cooling garments were ineffective. Most studies failed to document or report adverse events. Low participant numbers in each study limited the statistical power of certain reported trends and lack of blinding could potentially have introduced either participant or researcher bias in some studies.

Conclusions

Current evidence indicates cold water immersion may be the most effective method of pre-cooling to improve endurance performance in hot conditions, although practicality must be considered. Ice slurry ingestion appears to be the most promising practical alternative. Interestingly, cooling garments appear of limited efficacy, despite their frequent use. Mechanisms behind effective pre-cooling remain uncertain, and optimal protocols have yet to be established. Future research should focus on standardizing exercise performance protocols, recruiting larger participant numbers to enable direct comparisons of effectiveness and practicality for each method, and ensuring potential adverse events are evaluated.

Background

Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches.

Methods

We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability.

Results

Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model.

Conclusions

Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models.

Background

Pandemic influenza is said to 'shift mortality' to younger age groups; but also to spare a subpopulation of the elderly population. Does one of these effects dominate? Might this have important ramifications?

Methods

We estimated age-specific excess mortality rates for all-years for which data were available in the 20th century for Australia, Canada, France, Japan, the UK, and the USA for people older than 44 years of age. We modeled variation with age, and standardized estimates to allow direct comparison across age groups and countries. Attack rate data for four pandemics were assembled.

Results

For nearly all seasons, an exponential model characterized mortality data extremely well. For seasons of emergence and a variable number of seasons following, however, a subpopulation above a threshold age invariably enjoyed reduced mortality. 'Immune escape', a stepwise increase in mortality among the oldest elderly, was observed a number of seasons after both the A(H2N2) and A(H3N2) pandemics. The number of seasons from emergence to escape varied by country. For the latter pandemic, mortality rates in four countries increased for younger age groups but only in the season following that of emergence. Adaptation to both emergent viruses was apparent as a progressive decrease in mortality rates, which, with two exceptions, was seen only in younger age groups. Pandemic attack rate variation with age was estimated to be similar across four pandemics with very different mortality impact.

Conclusions

In all influenza pandemics of the 20th century, emergent viruses resembled those that had circulated previously within the lifespan of then-living people. Such individuals were relatively immune to the emergent strain, but this immunity waned with mutation of the emergent virus. An immune subpopulation complicates and may invalidate vaccine trials. Pandemic influenza does not 'shift' mortality to younger age groups; rather, the mortality level is reset by the virulence of the emerging virus and is moderated by immunity of past experience. In this study, we found that after immune escape, older age groups showed no further mortality reduction, despite their being the principal target of conventional influenza vaccines. Vaccines incorporating variants of pandemic viruses seem to provide little benefit to those previously immune. If attack rates truly are similar across pandemics, it must be the case that immunity to the pandemic virus does not prevent infection, but only mitigates the consequences.

Active smoking is a recognized risk factor of various infectious diseases. In a systematic review published in BMC Public Health, Murray et al. demonstrated that exposure to passive smoking significantly increased the risk of meningococcal disease among children. Their review especially highlights that the risk remains high even if the exposure occurs during pregnancy or after birth, although the authors could not disentangle the independent effects of smoking during pregnancy from those in the postnatal period. How passive smoking increases the risk of childhood meningococcal disease is not precisely known. Both exposure to 'smoke', or 'smokers' (who are highly susceptible to pharyngeal carriage of meningococci) are postulated mechanisms, but unfortunately very few studies have examined the risk of exposure by considering these two variables separately, and this therefore remains a research priority. Quitting may well be the mainstay of preventing tobacco-related hazards but the available global data suggest that most smokers are reluctant to quit. Among other interventions, immunizing children with a meningococcal conjugate vaccine could, theoretically, reduce the risk of meningococcal disease among children and their smoker household contacts through herd immunity.

See related article http://www.biomedcentral.com/1471-2458/12/1062

Background

We developed a Monte Carlo Markov model designed to investigate the effects of modifying cardiovascular disease (CVD) risk factors on the burden of CVD. Internal, predictive, and external validity of the model have not yet been established.

Methods

The Rotterdam Ischemic Heart Disease and Stroke Computer Simulation (RISC) model was developed using data covering 5 years of follow-up from the Rotterdam Study. To prove 1) internal and 2) predictive validity, the incidences of coronary heart disease (CHD), stroke, CVD death, and non-CVD death simulated by the model over a 13-year period were compared with those recorded for 3,478 participants in the Rotterdam Study with at least 13 years of follow-up. 3) External validity was verified using 10 years of follow-up data from the European Prospective Investigation of Cancer (EPIC)-Norfolk study of 25,492 participants, for whom CVD and non-CVD mortality was compared.

Results

At year 5, the observed incidences (with simulated incidences in brackets) of CHD, stroke, and CVD and non-CVD mortality for the 3,478 Rotterdam Study participants were 5.30% (4.68%), 3.60% (3.23%), 4.70% (4.80%), and 7.50% (7.96%), respectively. At year 13, these percentages were 10.60% (10.91%), 9.90% (9.13%), 14.20% (15.12%), and 24.30% (23.42%). After recalibrating the model for the EPIC-Norfolk population, the 10-year observed (simulated) incidences of CVD and non-CVD mortality were 3.70% (4.95%) and 6.50% (6.29%). All observed incidences fell well within the 95% credibility intervals of the simulated incidences.

Conclusions

We have confirmed the internal, predictive, and external validity of the RISC model. These findings provide a basis for analyzing the effects of modifying cardiovascular disease risk factors on the burden of CVD with the RISC model.

Background

More than a million diagnostic cardiac catheterizations are performed annually in the US for evaluation of coronary artery anatomy and the presence of atherosclerosis. Nearly half of these patients have no significant coronary lesions or do not require mechanical or surgical revascularization. Consequently, the ability to rule out clinically significant coronary artery disease (CAD) using low cost, low risk tests of serum biomarkers in even a small percentage of patients with normal coronary arteries could be highly beneficial.

Methods

Serum from 359 symptomatic subjects referred for catheterization was interrogated for proteins involved in atherogenesis, atherosclerosis, and plaque vulnerability. Coronary angiography classified 150 patients without flow-limiting CAD who did not require percutaneous intervention (PCI) while 209 required coronary revascularization (stents, angioplasty, or coronary artery bypass graft surgery). Continuous variables were compared across the two patient groups for each analyte including calculation of false discovery rate (FDR ≤ 1%) and Q value (P value for statistical significance adjusted to ≤ 0.01).

Results

Significant differences were detected in circulating proteins from patients requiring revascularization including increased apolipoprotein B100 (APO-B100), C-reactive protein (CRP), fibrinogen, vascular cell adhesion molecule 1 (VCAM-1), myeloperoxidase (MPO), resistin, osteopontin, interleukin (IL)-1β, IL-6, IL-10 and N-terminal fragment protein precursor brain natriuretic peptide (NT-pBNP) and decreased apolipoprotein A1 (APO-A1). Biomarker classification signatures comprising up to 5 analytes were identified using a tunable scoring function trained against 239 samples and validated with 120 additional samples. A total of 14 overlapping signatures classified patients without significant coronary disease (38% to 59% specificity) while maintaining 95% sensitivity for patients requiring revascularization. Osteopontin (14 times) and resistin (10 times) were most frequently represented among these diagnostic signatures. The most efficacious protein signature in validation studies comprised osteopontin (OPN), resistin, matrix metalloproteinase 7 (MMP7) and interferon γ (IFNγ) as a four-marker panel while the addition of either CRP or adiponectin (ACRP-30) yielded comparable results in five protein signatures.

Conclusions

Proteins in the serum of CAD patients predominantly reflected (1) a positive acute phase, inflammatory response and (2) alterations in lipid metabolism, transport, peroxidation and accumulation. There were surprisingly few indicators of growth factor activation or extracellular matrix remodeling in the serum of CAD patients except for elevated OPN. These data suggest that many symptomatic patients without significant CAD could be identified by a targeted multiplex serum protein test without cardiac catheterization thereby eliminating exposure to ionizing radiation and decreasing the economic burden of angiographic testing for these patients.

Background

According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression.

Methods

We undertook a systematic literature search of MEDLINE, PsycINFO and Embase in May 2012. We included studies analyzing the depression criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) of adults with MDD in latent variable analyses.

Results

In total, 1176 articles were retrieved, of which 20 satisfied the inclusion criteria. These reports described a total of 34 latent variable analyses: 6 confirmatory factor analyses, 6 exploratory factor analyses, 12 principal component analyses, and 10 latent class analyses. The latent class techniques distinguished 2 to 5 classes, which mainly reflected subgroups with different overall severity: 62 of 71 significant differences on symptom level were congruent with a latent class solution reflecting severity. The latent class techniques did not consistently identify specific symptom clusters. Latent factor techniques mostly found a factor explaining the variance in the symptoms depressed mood and interest loss (11 of 13 analyses), often complemented by psychomotor retardation or fatigue (8 of 11 analyses). However, differences in found factors and classes were substantial.

Conclusions

The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes. The wide diversity of identified factors and classes might result either from the absence of patterns to be found, or from the theoretical and modeling choices preceding analysis.